Vetted Matplotlib Professionals

“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”

“Robotics software engineer with hands-on experience building a vision-guided grasping pipeline on a 7-DOF Franka arm, implementing gradient-based IK with null-space optimization and RRT* motion planning in ROS1. Strong in sim-to-real deployment and real-world debugging—addressed frame misalignment via hand-eye calibration and centralized TF configuration, and reduced replanning/jitter by tuning a weighted pose filter using rosbag replay and variance/grasp-time metrics. Also built an ESP32-based mobile robot architecture combining embedded decision-tree control with WiFi/web high-level commands.”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

“IAM/platform engineer with experience at DocuSign and Siemens who ships production-grade systems end-to-end: built a secure AWS serverless internal employee-profile API (OAuth2/Cognito/WAF) that cut data retrieval from weeks to near-instant and sustained ~2,800 RPS at ~75 ms. Also delivered production AI workflows, including a GPT-4o + Playwright crypto-scam detection agent and an NLP ticket-routing system improved to ~86.7% accuracy with strong monitoring and incident mitigation practices.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”

“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”

“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”